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1,173 results for “pandemics”
Single-cell analysis of the epigenomic and transcriptional landscape of innate immunity to seasonal and adjuvanted pandemic influenza vaccination in humans [ATAC-seq]
GEO Series GSE166180. Homo sapiens. 51 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
A Metagenomics Analysis of Pandemic 2009 H1N1 Infection in Patients in North America
GEO Series GSE24034. Homo sapiens; Viruses. 29 samples. Type: Expression profiling by array.
The 1918 PB2 protein, not HA, enhances the virulence of an avian influenza virus closely related to the 1918 pandemic virus through the inhibition of wnt signaling.
GEO Series GSE70502. Mus musculus. 66 samples. Type: Expression profiling by array.
Temporal Response to seasonal and pandemic H1N1 infection in human DCs - Donor2
GEO Series GSE55277. Homo sapiens. 120 samples. Type: Expression profiling by array.
Differential microRNA expression profiles of mouse lungs infected with 2009 pandemic H1N1 influenza virus and seasonal H1N1 virus
GEO Series GSE46087. Mus musculus. 15 samples. Type: Non-coding RNA profiling by array.
Multi-omics analysis of human placentas from first year of the COVID-19 pandemic reveals sex-dependant effects
GEO Series GSE318446. Homo sapiens. 66 samples. Type: Expression profiling by high throughput sequencing.
1918 pandemic influenza virus and pneumococcus co-infection results in activation of coagulation and widespread pulmonary microvascular thrombosis in mice and humans
GEO Series GSE70445. Mus musculus. 34 samples. Type: Expression profiling by array.
Enhanced TLR7-dependent production of type I interferon by pDCs underlies pandemic chilblains
GEO Series GSE289528. Homo sapiens. 13 samples. Type: Expression profiling by array.
Hosts responses in critical disease caused by pandemic H1N1
GEO Series GSE21802. Homo sapiens. 40 samples. Type: Expression profiling by array.
Comparative transcriptomic analysis of acute host responses during 2009 pandemic H1N1 influenza infection in mouse, macaque, and swine (swine dataset)
GEO Series GSE40847. Sus scrofa. 9 samples. Type: Expression profiling by array.
Comparative transcriptomic analysis of acute host responses during 2009 pandemic H1N1 influenza infection in mouse, macaque, and swine (mouse dataset)
GEO Series GSE40091. Mus musculus. 17 samples. Type: Expression profiling by array.
Characterizing the transcriptomic response of mice infected with A/Anhui/01/2013 (H7N9), A/Netherlands/219/2003 (H7N7), and A/Vietnam/1203/2004 (H5N1), and a pandemic H1N1 human virus, A/Mexico/4482/2
GEO Series GSE54048. Mus musculus. 68 samples. Type: Expression profiling by array.
logfiles to accompany Managing Coronavirus Disease 2019 Spread With Voluntary Public Health Measures: Sweden as a Case Study for Pandemic Control
<p>Logfiles corresponding to public health interventions 1-5 as tested in Kamerlin and Kasson, 2020 are in logfiles_5day_bulk.tgz (corresponding to 5-day doubling time). Logfiles corresponding to the validation plots in the supplement are in valid_logfiles.tgz.</p> <p>Logfiles are provided as primary data. The parsed data in .mat form are available in a separate Zenodo repository referenced in the corresponding paper, cover the full set of voluntary public-health behaviors, and are anticipated to be more helpful in re-analysis.</p>
The effects of social isolation among elderly people in nursing homes in Austria during the Covid-19 pandemic
<p>This dataset contains data from a study in which we examined the extent to which isolation affected degenerative cognitive performance, depression and the subjective quality of life of people with dementia. 49 participants (58-94 of age) living in nursing homes were tested before and after the social isolation from the COVID-19 pandemic in cognitive performance, depressed mood, anxiety and subjective life quality.</p>
Predictors of the use of physician's televisits during the COVID-19 pandemic in Poland
<p>The dataset used for the analysis in the paper "Predictors of the use of physician’s televisits during the COVID-19 pandemic in Poland"</p>
View of an Evolving Pandemic: Changes in the Relationship between Clinical Cases and Levels of SARS-CoV-2 RNA in Colorado Wastewater
<p>This repository contains datasets utilized in the research paper titled "View of an Evolving Pandemic: Changes in the Relationship between Clinical Cases and Levels of SARS-CoV-2 RNA in Colorado Wastewater," published in ACS ES&T Water. The datasets include SARS-CoV-2 wastewater monitoring data alongside COVID caseload data for 23 locations in Colorado over a 22-month period.</p>
Supplement to: Global impact of the COVID-19 pandemic on stroke care and intravenous thrombolysis
<p><span><span><span><span><span><span><span><span><span><span><span><b>Objective: </b></span></span></span></span></span></span></span></span></span></span></span></p> <p><span><span><span><span><span><span><span><span><span><span><span>The objectives of this study were to measure the global impact of the pandemic on the volumes for intravenous thrombolysis (IVT), IVT transfers, and stroke hospitalizations over 4 months at the height of the pandemic (March 1 to June 30, 2020) compared with two control 4-month periods. </span></span></span></span></span></span></span></span></span></span></span></p> <p><span><span><span><span><span><span><span><span><span><span><span><b>Methods:</b></span></span></span></span></span></span></span></span></span></span></span></p> <p><span><span><span><span><span><span><span><span><span><span><span>We conducted a cross-sectional, observational, retrospective study across 6 continents, 70 countries, and 457 stroke centers. Diagnoses were identified by their ICD-10 codes and/or classifications in stroke databases. </span></span></span></span></span></span></span></span></span></span></span></p> <p><span><span><span><span><span><span><span><span><span><span><span><b>Results:</b></span></span></span></span></span></span></span></span></span></span></span></p> <p><span><span><span><span><span><span><span><span><span><span><span>There were 91,373 stroke admissions in the 4 months immediately before compared to 80,894 admissions during the pandemic months, representing an 11.5% (95%CI, -11.7 to - 11.3, p<0.0001) decline. There were 13,334 IVT therapies in the 4 months preceding compared to 11,570 procedures during the pandemic, representing a 13.2% (95%CI, -13.8 to -12.7, p<0.0001) drop. Interfacility IVT transfers decreased from 1,337 to 1,178, or an 11.9% decrease (95%CI, -13.7 to -10.3, p=0.001). Recovery of stroke hospitalization volume (9.5%, 95%CI 9.2-9.8, p<0.0001) was noted over the two later (May, June) versus the two earlier (March, April) pandemic months. There was a 1.48% stroke rate across 119,967 COVID-19 hospitalizations. SARS-CoV-2 infection was noted in 3.3% (1,722/52,026) of all stroke admissions. </span></span></span></span></span></span></span></span></span></span></span></p> <p><span><span><span><span><span><span><span><span><span><span><span><b>Conclusions: </b></span></span></span></span></span></span></span></span></span></span></span></p> <p><span><span><span><span><span><span><span><span><span><span><span>The COVID-19 pandemic was associated with a global decline in the volume of stroke hospitalizations, IVT, and interfacility IVT transfers. Primary stroke centers and centers with higher COVID19 inpatient volumes experienced steeper declines. Recovery of stroke hospitalization was noted in the later pandemic months.</span></span></span></span></span></span></span></span></span></span></span></p>
Data from: Despite short-lived changes, COVID-19 pandemic had minimal large-scale impact on citizen science participation in India
<p>This dataset contains the filtered and processed data files used in the analyses for the paper titled "Despite short-lived changes, COVID-19 pandemic had minimal large-scale impact on citizen science participation in India". These are <code>.Rdata</code> files that can be readily imported into the R environment (all analyses were performed in the R environment using RStudio).</p> <p>It is important to note that these data files were derived from publicly available datasets, and that the entire analytical workflow is reproducible (see <a href="https://github.com/rikudoukarthik/covid-ebirding/blob/main/README.md">the Github repository</a> for more details). Below is a summary of the datasets used:</p> <h2>Data</h2> <p>The analysis is centred around eBird data from India, which is publicly available but is a very large file.</p> <ul> <li><a href="https://ebird.org/data/download/ebd" rel="nofollow"><em>eBird Basic Dataset (EBD)</em></a><em>, relMay-2022</em>: The current version of this dataset is much more recent and contains data after May 2022, but filtering it should produce a fairly similar dataset to the ones used in this study, with only minor changes. (The processed version of this EBD ready for analyses is <code>data0_MY_d_slice.RData</code>).</li> <li><em>Shapefiles</em>: <code>maps_sf.RData</code> contains admin unit polygons, and <code>grids_st_sf.RData</code> contains country-wide grids of multiple resolutions.</li> <li><a href="https://lpdaac.usgs.gov/products/mcd12q1v006/" rel="nofollow"><em>MODIS Land Cover Type Product MCD12Q1</em></a>: The processed data is available as <code>rast_UNU.RData</code>, but deleting this before starting the analysis will force fresh LULC data files from MODIS to be produced from the script.</li> <li><em>Timeline of COVID classification</em>: Available <code>covid_classification.csv</code>. Classification of the timeline of interest into various COVID categories.</li> </ul> <h2>Abstract</h2> <p>Many parts of the world lack the large and coordinated volunteer networks required for systematic monitoring of bird populations. In these regions, citizen science (CS) programmes offer an alternative with their semi-structured data, but the utility of these data is contingent on how, where, and how comparably birdwatchers watch birds, year on year. Trends inferred from the data can be confounded during years when birdwatchers may behave differently, such as during the COVID-19 pandemic. We wanted to ascertain how the data uploaded from India to one such CS platform, eBird, were impacted by this deadly global pandemic. To understand whether eBird data from the pandemic years in India are comparable to data from adjacent years, we explored several characteristics of the data, such as how often people watched birds in groups or at public locations, at multiple spatial and temporal scales. We found that the volume of data generated increased during the pandemic years 2020--21 compared to 2019. Data characteristics changed largely only during the peak pandemic months (April--May 2020 and April--May 2021) associated with high fatality rates and strict lockdowns. These changes in data characteristics (e.g., greater site fidelity and less group birding) were possibly due to the decreased human mobility and social interaction in these periods. The data from the remainder of these restrictive years remained similar to those of the adjacent years, thereby reducing the impact of the aberrant peak months on any annual inference. Our findings show that birdwatchers in India as contributors to CS rapidly returned to their pre-pandemic behaviour, and that the effects of the pandemic on birdwatching effort and birdwatcher behaviour are scale- and context-dependent. In summary, eBird data from the pandemic years in India remain useful for abundance trend estimation and similar large-scale applications, but will benefit from preliminary data quality checks when utilised at a fine scale.</p>
Tabular Dataset for Pandemic Fiction and AMR Timeline
<p>Events, Dates, and Texts used to develop the Pandemic Fiction timeline in Figure 1 of “ 'They are increasing': The Eclipsed Pandemic and Literature of Antimicrobial Resistance". Primary and Secondary sources to for historical events are presented din Appendix I. Secondary sources used to curate representative pandemic fiction list presented in Appendix II.</p>
Figure 2 from: Ivanova V, Pavlov D, Assenova T, Terzieva E, Milushewa P, Djemadan A, Vladimirova G, Dimitrova M, Kamusheva M (2021) COVID-19 pandemic impact on the pharmaceutical sector in Bulgaria. Pharmacia 68(3): 713-720. https://doi.org/10.3897/pharmacia.68.e71638
Figure 2 Sectors negatively affected by COVID-19.
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.